Postdoctoral Scholar in DFT/Machine Learning for Molecular Target Selection

at  The University of Calgary

Description
Area: DFT/Machine Learning for Molecular Target Selection
Duration: 16 months
Start date: ASAP
Annual Stipend: 55,000$CAD per annum, including University of Calgary Plan C health benefits
The Department of Chemistry in the Faculty of Science at the University of Calgary is accepting applications for a Postdoctoral Scholar in Machine Learning for Molecular Target Selection.

JOB DESCRIPTION:

Many areas of current chemical research require the synthesis of bespoke molecules to achieve a particular goal. Whether that goal involves catalysis, medicinal chemistry, or renewable energy research, synthesizing and testing new molecules is an expensive endeavor. In collaboration with several other laboratories on campus, the Van Humbeck group is aiming to use the modern tools of computational chemistry and machine learning to help prioritize targets for synthesis and testing. We are seeking a Postdoctoral scholar who can perform DFT calculations and integrate the results into machine learning workflows to help support several ongoing collaborations across fields of chemistry.

QUALIFICATIONS:

Applicants must hold (or will be receiving shortly) a PhD in Chemistry. The critical skills necessary for success in this position include: (i) expertise in running DFT calculations, as shown by peer-reviewed publications; (ii) experience building machine learning workflows in PyTorch, as shown by publications or found in GitHub repositories; (iii) experience working in collaborative teams featuring both experimental and computational chemists.

Please refer the Job description for details